NINE SLOUGHS: PROFILING THE CLIMATE HISTORY OF THE YUAN AND MING DYNASTIES, 1260–1644<sup>†</sup>
Bibliographic record
Abstract
I have written this essay to address what I regard as a pressing need among China historians for a stronger model of climate change and its impact on state and society during the imperial period. We have all become acutely conscious of climate change as an element of our own world, yet few of us have considered the impact of climate, particularly climate change, on our subjects of study. China is not without its climate historians, and yet the collective research is still in an early phase. Aware of this problem for some time, I published preliminary findings in the form of a chronological profile of climate anomalies through the Yuan and Ming dynasties in 2010. Burying my findings in a textbook has meant that the periodization offered there has captured the interest of some students but gone largely ignored by scholars in the field. Since then I have done further research and have revised some of those findings, and would now like to offer a fuller presentation of methods and findings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".